Development a case-based classifier for predicting highly cited papers

Mingyang Wang

doi:10.1016/j.joi.2012.06.002

Development a case-based classifier for predicting highly cited papers

2012-10-01

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AI

DOI：10.1016/j.joi.2012.06.002

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Abstract

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In this paper, we discussed the feasibility of early recognition of highly cited papers with citation prediction tools. Because there are some noises in papers' citation behaviors, the soft fuzzy rough set (SFRS), which is well robust to noises, is introduced in constructing the case-based classifier (CBC) for highly cited papers. After careful design that included: (a) feature reduction by SFRS; (b) case selection by the combination use of SFRS and the concept of case coverage; (c) reasoning by two classification techniques of case coverage based prediction and case score based prediction, this study demonstrates that the highly cited papers could be predicted by objectively assessed factors. It shows that features included the research capabilities of the first author, the papers' quality and the reputation of journal are the most relevant predictors for highly cited papers. (C) 2012 Elsevier Ltd. All rights reserved.

Keywords:

Highly cited papers

Prediction

Case-based classifier

Development a case-based classifier for predicting highly cited papers

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